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Annals of Clinical & Laboratory Science 35:46-53 (2005)
© 2005 Association of Clinical Scientists

Predicting Clinical Outcomes in Peritoneal Dialysis Patients Using Small Solute Modeling

Justin Westhuyzen1, Karen Mills2 and Helen Healy2
1 Conjoint Renal Laboratory, Queensland Health Pathology Service, and2 Department of Renal Medicine, Royal Brisbane and Women’s Hospital, Herston, Queensland, Australia

Address correspondence to Dr Helen Healy, Department of Renal Medicine, Royal Brisbane & Women’s Hospital, Herston, Queensland 4029, Australia; tel 617 3636 8576; fax 617 3636 8572; e-mail helen_healy{at}health.qld.gov.au.


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 References
 
The power of published models of dialysis adequacy to predict clinical outcomes in renal failure patients maintained on continuous ambulatory peritoneal dialysis (CAPD) is controversial. Inflammation may be an important predictor of morbidity and mortality in CAPD. Baseline data from a 2-yr prospective, longitudinal study of peritoneal dialysis adequacy were analysed. Baseline measures of dialysis adequacy (urea clearance [Kt/V], efficiency number [EN], dialysis index [DI], dialysate-plasma creatinine ratio [D/Pcreat], creatinine clearance [CrCl weekly PD]) as predictors of outcome were investigated by univariate analysis and by multiple logistic regression modelling. Baseline nutritional and inflammatory markers were also tested as predictors of outcomes. Outcomes were patient survival and technique failure over the succeeding 2 yr. Fifty-three patients consented to the study; 7 patients were unsuitable. Only 6 patients completed the study (13%). Non-survivors (n = 6) had lower protein catabolic rates and lower serum albumin concentrations, and higher C-reactive protein (CRP) levels at baseline than the patients who survived (p <0.05), but there were no differences in any of the measures of dialysis adequacy. The patient group that developed technique failure (n = 9) had significantly higher D/Pcreat (p = 0.037) at baseline. Serum albumin and CRP at study entry were significant negative and positive predictors of death respectively (p <0.05). No baseline variable achieved significance as a predictor of technique failure in the patient cohort. In conclusion, dialysis dose descriptors are poor predictors of clinical outcomes in CAPD patients. Inflammatory and nutritional markers such as CRP and albumin may be more important in predicting patient outcomes than measures of peritoneal small solute clearance.

(received 14 December 2004; accepted 17 December 2004)

Keywords: peritoneal dialysis, urea kinetics, patient survival, technique failure, albumin, C-reactive protein


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 References
 
Urea kinetic modelling using the parameter Kt/V has gained wide acceptance as a tool for assessing dialysis prescription and future dialysis requirements of patients with renal failure maintained on hemodialysis. In contrast, the measurement and description of dialysis adequacy for peritoneal dialysis (PD) remains a controversial area [1–8]. A number of measures of dialysis adequacy have been developed, including the Peritoneal Equilibration Test (PET)[9], dialysis index (DI)[10], dialysate/plasma creatinine ratio (D/Pcreat), and the efficacy number (EN)[11]. These all have advantages and shortcomings [12].

The PET model [9] provides useful data for peritoneal solute and water transport kinetics but does not measure total body solute clearances and does not account for non-peritoneal transport including urinary losses. As a result, poor correlations between urea clearances (Kt/V) derived from the PET model and from urea kinetic modelling have been reported [12].

DI was specifically designed for PD [10], and this index measures the ratio between the actual daily dialysate drain and the prescribed daily dialysate drain volume (DDDV). DI reflects the fraction of urea nitrogen clearance achieved and can be used to evaluate both total urea nitrogen clearance and protein catabolic rate. However, the calculations are cumbersome, and the index has not gained wide acceptance.

The D/Pcreat is easily calculated but varies with dialysis dwell time [9]. Efficacy number [11] is based on daily creatinine clearance estimated from the PET and may be more useful in determining adequacy of PD than the D/P ratio of creatinine alone.

Dialysis adequacy (however defined) should be reflected in significant biological endpoints such as patient mortality, hospitalisation rates, and quality of life. The National Cooperative Dialysis Study of hemodialysis patients demonstrated that high blood concentrations of urea were associated with poor patient outcomes as described by these factors [13]. The relationship between high blood urea and patient outcomes in PD patients is less clear. Some studies support a relationship between low Kt/V and poor clinical outcomes and increased risk of dying [1,3,7,14], but others, including the landmark ADEMEX study, have refuted the power of Kt/V to describe the efficacy of PD treatment and to predict patient outcome [2,4,6,15,16].

In order to examine the association between dialysis adequacy and clinical endpoints, a prospective, longitudinal study of 2 yr duration was undertaken. In the present report, models of dialysis adequacy at entry were evaluated as predictors of death and technique failure in PD patients. Since the role of inflammation and malnutrition in patient morbidity and mortality has been increasingly recognised in recent years [1,17–23], plasma C-reactive protein (CRP) and albumin levels at entry were also investigated as predictors of outcome.


   Methods
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 Abstract
 Introduction
 Methods
Results
 Discussion
 References
 
Study design.  The present study analyses data from the prospective 2-yr trial of dialysis adequacy conducted in the Department of Renal Medicine, Royal Brisbane and Women’s Hospital (Brisbane, Australia). The study protocol was approved by the Human Research Ethics Committee of the Royal Brisbane and Women’s Hospital; written, informed consent was obtained from all study subjects.

Selection of patients.  Clinically stable adult patients treated with PD for 3 mo and <24 mo w e re eligible for inclusion. Patients with cardiovascular disease or active malignancy were excluded, as well as those with other conditions or therapy that in the opinion of the investigators might pose a risk to the patient or confound the study’s results. Fifty-three patients from Brisbane and surrounding towns consented to the study; 46 patients entered the study. Patients were treated using standard CAPD protocols and continued taking their usual medications.

Study protocol and assessments.  Patients were assessed at 3-mo intervals. Assessment included a standard peritoneal equilibration test (PET) as described by Twardowski [9], physical examination, biochemical studies, and patient interviews. Kt/V urea (total, expressed per wk), residual renal function (RRF) (ml/min), body surface area, and normalised protein catabolic rate (nPCR, expressed as g/Kg/day) were computed with "PD Adequest" software (Baxter Healthcare, USA).

In addition to Kt/V, dialysis adequacy was described by weekly PD creatinine clearance (L/1.73 m2 body surface area), efficacy number (EN), D/Pcreat and dialysis index (DI). EN was calculated according to Brandes et al [11]:


where Vd is the prescribed volume of exchanges per day (L) and ACPPD is the sum of peritoneal dialysis creatinine appearance over 24 hr and creatinine generation at constant extrarenal clearance:


where Dcr is the 4 hr dialysate creatinine concentration in mg/dl, V is the drain volume in dl, Scr is serum creatinine in mg/dl, and BW is body weight in Kg. DI was calculated by dividing actual DDDV (24 hr drain volume) by the required DDDV ([0.23 x IBW] – [2.6+1.44 x RRFml/min]) [10].

Quality of life (QOL) descriptors were tested as predictors of clinical outcome. We used the clinical assessment score (CAS), which considers symptoms indicative of inadequate dialysis [1], Short Form 36 (SF36) [24], and a co-morbidity index that scored the presence of diabetes, hypertension, and atherosclerotic disease (range 0 – 3) [25].

Urea, creatinine, serum albumin, and C-reactive protein (CRP) were measured with automated analysers using standard laboratory methods.

Data analysis.  Data are presented as mean ± SEM or median (range) for parametric and non-parametric data respectively. Differences between groups were sought using Student’s t-test or the Mann-Whitney Rank Sum test for parametric and non-parametric data respectively. The {chi}2 test with Yates’ correction was used to test the significance of differences in qualitative variables and proportions. Linear correlations were sought using the Pearson product-moment method. Multiple logistic regression modelling was used to explore predictors of outcome (death, technique failure) in the study patients. Baseline variables added to the models included selected measures of dialysis adequacy (Kt/V, creatinine clearance, RRF, DI, EN, D/Pcreat) and factors that were significant or close to reaching significance (Tables 1Go–3Go) (ie, age, BMI, nPCR, albumin, CRP, and CAS). Values of p <0.05 were considered significant.


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  		Table 1. Baseline measures of dialysis adequacy, nutritional parameters, and demographic variables in non-survivors and survivors (means ± SEM)
 

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  		Table 3. Baseline characteristics of patients who developed technique failure during the study, including those who were withdrawn due to inadequate therapy (means ± SEM)
 

   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 References
 
During the 2-yr study, 40 patients were lost from the trial. There were 6 deaths (13%), and only 6 patients completed the study. The common reasons for withdrawal were technique failure (19.6%), inadequate therapy (17.4%), non-compliance (8.7%), and withdrawal by patient (6.5%).

Baseline interrelationships.  Positive correlation was found between Kt/V and RRF (r = 0.614; p <0.0001), and between Kt/V and nPCR (r = 0.517; p <0.001). RRF was also positively correlated with nPCR (r = 0.326; p = 0.027). The association of Kt/V and DI (which has the same 3 determinants as Kt/V, namely body weight, RRF, and daily dialysate drainage volume) is self-evident and was not tested. Kt/V was highly correlated with creatinine clearance (r = 0.739; p <0.001) and EN (r = 0.587; p <0.001), but not with D/Pcreat (p = 0.559).

Univariate analysis.  The baseline characteristics of the survivors and non-survivors and those who were withdrawn from the study because of technique failure were examined. There were no differences between survivors and non-survivors with respect to any of the models of dialysis adequacy, although dialysis adequacy tended to be lower in non-survivors (Table 1Go). However, the group who died tended to be older (p = 0.053), and had significantly lower nPCR and lower serum albumin concentrations, as well as higher CRP levels (p <0.05).

The relation of technique failure to baseline characteristics was studied in 2 ways. In the first, technique failure per se was examined (Table 2Go). With the exception of D/Pcreat, which was higher in the technique failure group (p = 0.037), there were no differences in any of the dialysis measures, nutritional and biochemical parameters, nor in clinical assessment score (CAS)(p >0.19) between the 2 groups.


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  		Table 2. Baseline characteristics of patients who developed technique failure per se during the study (means ± SEM)
 
In the second analysis, patients who were withdrawn because of inadequate therapy were included in an expanded technique failure group (Table 3Go). In this case, D/Pcreat only approached statistical significance (p = 0.057), as did BMI (tending to be higher in the technique failure group, p = 0.061). The other baseline parameters did not differ significantly in the patient groups.

Data modelling.  Multiple logistic regression modelling was used to explore predictors of death in the study patients. Baseline variables tested included age, time on dialysis, Kt/V, CRP, CAS, albumin, and nPCR. No significant models could be constructed that included Kt/V. The "best" model that included Kt/V also incorporated albumin and age (p >0.08). However, the combination of albumin, CRP, and CAS was significant (p = 0.034, 0.061, and 0.061 for the 3 variables respectively). Excluding CAS increased the significance of the model (p = 0.045 for albumin, and p = 0.031 for CRP; Table 4Go). While several other models could be constructed, the only significant factor in these cases was the "constant" (data not presented). Thus, albumin and CRP were the only 2 baseline variables that predicted death in the study cohort.


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  		Table 4. Death discriminant modelling (multiple logistic regression modelling with death as the dependent variable)
 
Multiple logistic regression modelling was also performed with technique failure as the dependent variable (Table 5Go). The only models that could be constructed with technique failure per se included D/Pcreat or D/Pcreat, and DI as the independent variables. In these models, the "constant" was the only significant factor (p = 0.021 and 0.022 respectively). The only model that could be constructed with the expanded technique failure group (which included the patients who were withdrawn because of inadequate dialysis) included D/Pcreat and BMI as the independent variables. Once again, the "constant" was the only significant factor (p = 0.019). In summary, no baseline variable achieved significance as a predictor of technique failure in the patient cohort.


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  		Table 5. Multiple logistic regression modelling with technique failure per se (models 1,2) or technique failure including withdrawals due to inadequate dialysis (model 3) as the dependent variable(s)
 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
References
 
Not surprisingly, significant correlations were found between the various measures of dialysis adequacy. nPCR (a composite measure of nitrogen losses) also correlated strongly with Kt/V (a measure of urea clearance), and with RRF. Blake et al [2] also found a strong correlation between Kt/V and nPCR and suggested that the dialytic dose represented by Kt/V may be an important determinant of a patient’s protein intake. This could operate by a homeostatic mechanism that responds to uremia by suppressing appetite and hence protein intake [2]. The positive correlation observed in the present study between RRF and nPCR (r = 0.326, p = 0.027) may be part of the homeostatic feedback loops.

The role of the inflammatory response in dialysis patient morbidity and mortality has been recognised in recent years [17–22]. Inflammation is associated with loss of muscle mass, changes in plasma proteins including acute phase reactants [21], and increased risk of adverse events in dialysis patients [22]. The inflammatory marker CRP in particular has been proposed as a cardiovascular risk factor [23]. Previously, we reported an association between raised CRP and cardiovascular disease in hemodialysis patients [19]. In the present study, CRP was a positive predictor of death (p = 0.031) in a model that included serum albumin, which was a significant negative predictor of death (p = 0.045). Interestingly, this was observed in a cohort that specifically excluded patients with overt cardiovascular disease. Among the various causes of inflammation, vascular site infection in hemodialysis patients and peritonitis in PD patients are clinically important, but the causes of inflammation in dialysis patients are often not identified.

While inflammatory stimuli result in increased concentrations of positive acute phase reactants such as CRP, negative acute phase reactants such as albumin are suppressed. Inflammation (represented by CRP) has been suggested to be a better predictor of low serum albumin levels than nutritional parameters in both hemodialysis and PD patients [26,27]. In the present study, serum albumin was a significant negative predictor of death when considered with CRP. Indeed, these acute phase reactants were the only combination of variables that predicted death in our study cohort. This observation is in line with previous studies that found low serum albumin to be a strong predictor of death in hemodialysis [28–30] as well as PD patients [1,31,32]. While Teehan et al [1] and others [33,34] found serum albumin and Kt/V to be negative predictors of death, Kt/V did not reach significance as an independent predictor in our study, possibly due to lack of power as a consequence of the high drop-out rate.

The relevance of Kt/V as a predictor of important clinical outcomes like mortality and technique failure has been debated for many years. Blake et al [2] found that Kt/V did not predict mortality, technique failure, and other measures of morbidity, and concluded that Kt/V and the related DI were not good measures of dialysis adequacy in PD patients. The recent ADEMEX study which followed 965 patients for a minimum of 2 yr concurs, and makes the point that the survival benefit of PD may be effect-neutral within the range of clearances currently achieved, at least at the group level [16]. Perhaps Kt/V correlates with poor patient outcomes when Kt/V is below a threshold value (<1.5 or <1.7) but not when urea clearance is higher [8]. In the study of Davies et al [35], inclusion of plasma albumin and Kt/V increased the significance of the Cox model, but co-morbidity and age were the only statistically significant predictors of mortality.

In contrast to the above, several smaller studies that employed different research methods have supported an association between small solute removal and patient survival [25], including the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD Study) [36]. Although death was predicted by total urea or creatinine removal, the use of clearance parameters such as total Kt/V or creatinine clearance failed to reach statistical significance [36].

In summary, we found serum CRP and albumin to be more important than measures of dialysis adequacy in predicting patient outcomes in this patient group. Morbidity and mortality in CAPD patients may be more closely determined by inflammatory conditions associated, for example, with peritonitis and cardiovascular disease than by the adequacy of dialysis.


   Acknowledgments
 
We thank Mr Anthony Dique (Scientist), David Saltissi, M.D. (Acting Director), Paula Winch (Clinical Nurse), Pauline Nicholas (acting Clinical Nurse Consultant) and Margaret King (Clinical Nurse Consultant) for their contributions to the study design and valuable help in the early stages of this study. This study was supported in part by an Educational Grant from Baxter Healthcare Corp.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 

  1. Teehan BP, Schleifer CR, Brown JM, Sigler MH, Raimondo J. Urea kinetic analysis and clinical outcome on CAPD. A five year longitudinal study. Adv Perit Dial 1990;6:181–185.[Medline]
  2. Blake PG, Sombolos K, Abraham G, Weissgarten J, Pemberton R, Che GL, et al. Lack of correlation between urea kinetic indices and clinical outcomes in CAPD patients. Kidney Int 1991;39:700–706.[Medline]
  3. De Alvaro F, Bajo MA, Alvarez-Ude F, Vigil A, Molina A, Coronel G, et al. Adequacy of peritoneal dialysis: Does Kt/v have the same predictive value as in HD? A multicentre study. Adv Perit Dial 1992;8:93–97.[Medline]
  4. Blake PG, Balaskas E, Blake R, Oreopoulos DG. Urea kinetics has limited relevance in assessing adequacy of dialysis in CAPD. Adv Perit Dial 1992;8:65–70.[Medline]
  5. Brandes JC, Piering WG, Beres JA, Blumenthal SS, Fritsche C. Clinical outcome of continuous ambulatory peritoneal dialysis predicted by urea and creatinine kinetics. J Am Soc Nephrol 1992;2:1430–1435.[Abstract]
  6. Goodship THJ, Pabblick-Deetjen J, Ward MK, Wilkinson R. Adequacy of dialysis and nutritional status in CAPD. Nephrol Dial Transplant 1993;8:1366–1373.[Abstract/Free Full Text]
  7. Churchill DN. Adequacy of peritoneal dialysis: How much dialysis do we need? Kidney Int 1994;46:S2–6.
  8. Blake PG. Is quantification of dialysis by urea kinetics important in continuous ambulatory peritoneal dialysis? Perit Dial Int 1992;13:S212–214.[Medline]
  9. Twardowski ZJ, Nolph KD, Khanna R, Prowant BE, Ryan LP, Moore HL, et al. Peritoneal equilibration test. Perit Dial Bull 1987;7:138–147.
  10. Teehan BP, Schleifer CR, Sigler MH, Gilgore GS. A quantitative approach to the CAPD prescription. Perit Dial Bull 1985 (July–September):152–156.
  11. Brandes JC, Piering WF, Beres JA. A method to assess efficacy of CAPD: preliminary results. Adv Perit Dial 1990;6:192–196.[Medline]
  12. Fung L, Pollock CA, Caterson RJ, Mahoney JF, Waugh DA, Macadam C et al. Dialysis adequacy and nutrition determine prognosis in continuous ambulatory peritoneal dialysis patients. J Am Soc Nephrol 1996;7:737–744.[Abstract]
  13. The National Cooperative Dialysis Study. Kidney Int 1983;23:S1–122.
  14. Arkouche W, Delawari E, My H, Laville M, Abdullah E, Traeger J. Quantification of adequacy of peritoneal dialysis. Perit Dial Int 1993;13:S215–218.
  15. Mooraki A, Kliger AS, Goirbon-Brennan NL, Juergensen P. Brown E, Finkelstein FO. Weekly Kt/v urea and selected outcome criteria in 56 randomly selected CAPD patients. Adv Perit Dial 1993;9:92–96.[Medline]
  16. Paniagua R, Amato D, Vonesh E, Correa-Rotter R, Ramos A, Moran J et al. Effects of increased peritoneal clearances on mortality rates in peritoneal dialysis: ADEMEX, a prospective, randomized controlled trial. J Am Soc Nephrol 2002;13:1307–1319.[Abstract/Free Full Text]
  17. Stenvinkel P, Heimburger O, Paultre F, Diczfalusy U, Wang T, Berglund L, et al. Strong association between malnutrition, inflammation, and atherosclerosis in chronic renal failure. Kidney Int 1999;55;1899–1911.[Medline]
  18. Stenvinkel P. Inflammatory and atherosclerotic interactions in the depleted uremic patient. Blood Purif 2000;19:53–61.
  19. Healy H, Reith D, Morgan C, Clague A, Westhuyzen J. Are metalloproteins and acute phase reactants associated with cardiovascular disease in end-stage renal failure? Ann Clin Lab Sci 2000;30:297–306.
  20. Westhuyzen J, Healy H. Review: Biology and relevance of C-reactive protein in cardiovascular and renal disease. Ann Clin Lab Sci 2000;30:133–143.[Abstract]
  21. Kaysen, GA. The microinflammatory state in uraemia: causes and potential consequences. J Am Soc Nephrol 2001;12:1549–1557.[Abstract/Free Full Text]
  22. Stenvinkel, P. Inflammatory and atherosclerotic interactions in the depleted uremic patient. Blood Purif 2001;19:53–61.[Medline]
  23. Burnett JR, Watts GF, Vasikaran SD. C-reactive protein: a new cardiovascular risk factor? Med J Aus 2000;173:117–118.
  24. Diaz-Buxo JA, Lowrie EG, Lew NL, Zhang H, Lazarus JM. Quality-of-life evaluation using Short Form 36: comparison in hemodialysis and peritoneal dialysis patients. Am J Kidney Dis 2000;35:293–300.[Medline]
  25. Davis SJ, Phillips L, Russell GI. Peritoneal solute transport predicts survival on CAPD independently of residual renal function. Nephrol Dial Transplant 1998;13:962–968.[Abstract/Free Full Text]
  26. Han DS, Lee SW, Kang SW, Choi KH. Factors affecting low values of serum albumin in CAPD patients. Adv Perit Dial 1996;12:288–292.[Medline]
  27. Yeun JY, Kaysen GA. Acute phase reactants and peritoneal dialysate albumin loss are the main determinants of serum albumin in peritoneal dialysis patients. Am J Kidney Dis 1997;30:923–927.[Medline]
  28. Kaysen GA, Yeun J, Depner T. Albumin synthesis, catabolism and distribution in dialysis patients. Mineral Electrol Metab 1997;23:218–224.
  29. Goldwasser P, Mittman N, Antignani A, Burrell D, Michel MA, Collier J, et al. Predictors of mortality in hemodialysis patients. J Am Soc Nephrol 1993;3:1613–1622.[Abstract]
  30. Lowrie EG, Lew NL. Death risk in hemodialysis patients: The predictive value of commonly measured variables and an evaluation of death rate differences between facilities. Am J Kidney Dis 1990;15:458–482.[Medline]
  31. Avram MM, Fein PA, Bonomini L, Mittman N, Loutoby R, Avram DK, et al. Predictors of survival in continuous ambulatory peritoneal dialysis patients: A five year prospective study. Perit Dial Int 1996;16:S190–194.
  32. Diaz-Buxo JA, Lowrie EG, Lew NL, Zhang SM, Zhu X, Lazarus JM. Associates of mortality among peritoneal dialysis patients with special reference to peritoneal transport rates and solute clearance. Am J Kidney Dis 1999;33:523–534.[Medline]
  33. Maiorca R, Brunori G, Zubani R Cancarini GC, Manili L, Camerini C, et al. Predictive value of dialysis adequacy and nutritional indices for mortality and morbidity in CAPD and HD patients. A longitudinal study. Nephrol Dial Transplant 1995;10:2295–2305.[Abstract/Free Full Text]
  34. Rocco MV, Frankenfield DL, Prowant B, Frederick P, Flanigan MJ; Centers for Medicare & Medicaid Services Peritoneal Dialysis Core Indicators Study Group. Risk factors for early mortality in U.S. peritoneal dialysis patients: impact of residual renal function. Perit Dial Int 2002;22:371–379.[Abstract/Free Full Text]
  35. Davies SJ, Russell L, Bryan J, Phillips L, Russell GI. Comorbidity, urea kinetics, and appetite in continuous ambulatory peritoneal dialysis patients: their interrelationship and prediction of survival. Am J Kidney Dis 1995;26:353–361.[Medline]
  36. Jager KJ, Merkus MP, Dekker FW, Boeschoten EW, Tijssen JGP, Stevens P, et al, for the NECOSAD Study Group. Mortality and technique failure in patients starting chronic peritoneal dialysis: Results of the Netherlands Cooperative Study on the Adequacy of Dialysis. Kidney Int 1999;55:1476–1485.[Medline]



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